Genome-wide determinants of mortality and motor progression in Parkinson's disease.

There are 90 independent genome-wide significant genetic risk variants for Parkinson's disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments. We studied 6766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out genome-wide survival studies for time to a motor progression endpoint, defined by reaching Hoehn and Yahr stage 3 or greater, and death (mortality). There was a robust effect of the APOE ε4 allele on mortality in PD. We also identified a locus within the TBXAS1 gene encoding thromboxane A synthase 1 associated with mortality in PD. We also report 4 independent loci associated with motor progression in or near MORN1, ASNS, PDE5A, and XPO1. Only the non-Gaucher disease causing GBA1 PD risk variant E326K, of the known PD risk variants, was associated with mortality in PD. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, these may represent new candidates for disease modification in PD.

Prognosis of impulse control disorders in Parkinson's disease: a prospective controlled study.

BACKGROUND: The long-term prognosis of impulsive compulsive disorders (ICD) remains poorly studied in Parkinson's disease (PD). OBJECTIVE: Evaluating the natural history of ICD and its impact on PD symptoms including cognition and treatment adjustments. MATERIALS AND METHODS: We assessed PD patients at baseline (BL) with (BL-ICD+) or without (BL-ICD-) ICD despite dopamine agonist (DA) exposure of > 300 mg levodopa-equivalent daily dose for > 12 months at baseline and after more than two years of follow-up. ICD were assessed using the Ardouin's Scale of Behaviors in PD (ASBPD), cognition using the Mattis scale, and PD symptoms using the UPDRS score. Treatment adjustments, DA withdrawal-associated symptoms, and ICDs social consequences were recorded. RESULTS: 149 patients were included (78 cases and 71 controls), mean duration of follow-up was 4.4 ± 1 years. At baseline, psychiatric disorders were more common among BL-ICD + (42.3 vs 12.3% among BL-ICD-, p < 0.01). At follow-up, 53.8% of BL-ICD + were not ICD-free while 21.1% of BL-ICD- had developed ICD. BL-ICD + more frequently experienced akinesia (21.8 vs 8.5%, p = 0.043) and rigidity worsening (11.5 vs 1.4%, p = 0.019) following therapeutic modifications. Decision to decrease > 50% DA doses (12.8 vs 1.4%, p = 0.019) or to withdraw DA (19.2 vs 5.6%, p = 0.025) was more frequently considered among BL-ICD+ . At follow-up, the prevalence of cognitive decline was lower among BL-ICD + (19.2 vs 37.1%, p = 0.025). CONCLUSION: ICDs were associated with increased psychiatric burden at baseline and better cognitive prognosis. Most patients were still showing ICDs at the follow-up visit, suggesting ICD to be considered as a chronic, neuropsychiatric disorder.

Artificial Intelligence Radiographic Analysis Tool for Total Knee Arthroplasty.

BACKGROUND: The postoperative follow-up of a patient after total knee arthroplasty (TKA) requires regular evaluation of the condition of the knee through interpretation of X-rays. This rigorous analysis requires expertize, time, and methodical standardization. Our work evaluated the use of an artificial intelligence tool, X-TKA, to assist surgeons in their interpretation. METHODS: A series of 12 convolutional neural networks were trained on a large database containing 39,751 X-ray images. These algorithms are able to determine examination quality, identify image characteristics, assess prosthesis sizing and positioning, measure knee-prosthesis alignment angles, and detect anomalies in the bone-cement-implant complex. The individual interpretations of a pool of senior surgeons with and without the assistance of X-TKA were evaluated on a reference dataset built in consensus by senior surgeons. RESULTS: The algorithms obtained a mean area under the curve value of 0.98 on the quality assurance and the image characteristics tasks. They reached a mean difference for the predicted angles of 1.71° (standard deviation, 1.53°), similar to the surgeon average difference of 1.69° (standard deviation, 1.52°). The comparative analysis showed that the assistance of X-TKA allowed surgeons to gain 5% in accuracy and 12% in sensitivity in the detection of interface anomalies. Moreover, this study demonstrated a gain in repeatability for each single surgeon (Light's kappa +0.17), as well as a gain in the reproducibility between surgeons (Light's kappa +0.1). CONCLUSION: This study highlights the benefit of using an intelligent artificial tool for a standardized interpretation of postoperative knee X-rays and indicates the potential for its use in clinical practice.

Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson's Disease From Clinical and Genetic Data.

Goal: Impulse control disorders (ICDs) are frequent non-motor symptoms occurring during the course of Parkinson's disease (PD). The objective of this study was to estimate the predictability of the future occurrence of these disorders using longitudinal data, the first study using cross-validation and replication in an independent cohort. Methods: We used data from two longitudinal PD cohorts (training set: PPMI, Parkinson's Progression Markers Initiative; test set: DIGPD, Drug Interaction With Genes in Parkinson's Disease). We included 380 PD subjects from PPMI and 388 PD subjects from DIGPD, with at least two visits and with clinical and genetic data available, in our analyses. We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs. We quantified performance using the area under the receiver operating characteristic curve (ROC AUC) and average precision. We compared these models to a trivial model predicting ICDs at the next visit with the status at the most recent visit. Results: The recurrent neural network (PPMI: 0.85 [0.80 - 0.90], DIGPD: 0.802 [0.78 - 0.83]) was the only model to be significantly better than the trivial model (PPMI: ROC AUC = 0.75 [0.69 - 0.81]; DIGPD: 0.78 [0.75 - 0.80]) on both cohorts. We showed that ICDs in PD can be predicted with better accuracy with a recurrent neural network model than a trivial model. The improvement in terms of ROC AUC was higher on PPMI than on DIGPD data, but not clinically relevant in both cohorts. Conclusions: Our results indicate that machine learning methods are potentially useful for predicting ICDs, but further works are required to reach clinical relevance.

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping.

Motivation: Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. These diseases sometimes appear to have a significant component of heritability requiring explanation, and this missing heritability may be due to the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials might potentially have a role in guiding the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, and in disease subtyping, the two main strategies used are 1) the clustering of clinical data followed by posterior genetic analysis and 2) the concomitant clustering of clinical and genetic variables. Both of these strategies have limitations that we propose to address. Contribution: This work proposes an original method for disease subtyping on the basis of both longitudinal clinical variables and high-dimensional genetic markers via a sparse mixture-of-regressions model. The added value of our approach lies in its interpretability in relation to two aspects. First, our model links both clinical and genetic data with regard to their initial nature (i.e., without transformation) and does not require post-processing where the original information is accessed a second time to interpret the subtypes. Second, it can address large-scale problems because of a variable selection step that is used to discard genetic variables that may not be relevant for subtyping. Results: The proposed method was validated on simulations. A dataset from a cohort of Parkinson's disease patients was also analyzed. Several subtypes of the disease and genetic variants that potentially have a role in this typology were identified. Software availability: The R code for the proposed method, named DiSuGen, and a tutorial are available for download (see the references).

Exploratory analysis of the genetics of impulse control disorders in Parkinson's disease using genetic risk scores.

OBJECTIVE: To study the association between impulse control disorders (ICDs) in Parkinson's disease (PD) and genetic risk scores (GRS) for 40 known or putative risk factors (e.g. depression, personality traits). BACKGROUND: In absence of published genome-wide association studies (GWAS), little is known about the genetics of ICDs in PD. GRS of related phenotypes, for which large GWAS are available, may help shed light on the genetic contributors of ICDs in PD. METHODS: We searched for GWAS on European ancestry populations with summary statistics publicly available for a broad range of phenotypes, including other psychiatric disorders, personality traits, and simple phenotypes. We separately tested their predictive ability in two of the largest PD cohorts with clinical and genetic available: the Parkinson's Progression Markers Initiative database (N = 368, 33% female, age range = [33-84]) and the Drug Interaction With Genes in Parkinson's Disease study (N = 373, 40% female, age range = [29-85]). RESULTS: We considered 40 known or putative risk factors for ICDs in PD for which large GWAS had been published. After Bonferroni correction for multiple comparisons, no GRS or the combination of the 40 GRS were significantly associated with ICDs from the analyses in each cohort separately and from the meta-analysis. CONCLUSION: Albeit unsuccessful, our approach will gain power in the coming years with increasing availability of genotypes in clinical cohorts of PD, but also from future increase in GWAS sample sizes of the phenotypes we considered. Our approach may be applied to other complex disorders, for which GWAS are not available or limited.

Early cognitive decline after bilateral subthalamic deep brain stimulation in Parkinson's disease patients with GBA mutations.

BACKGROUND: Subthalamic nucleus deep brain stimulation (STN-DBS) has demonstrated its efficacy on motor complications in advanced Parkinson's disease (PD) but does not modify disease progression. Genetic forms of PD have been associated with different cognitive progression profiles. OBJECTIVE: To assess the effect of PD-related genetic mutations on cognitive outcome after STN-DBS. METHODS: Patients with STN-DBS were screened for LRRK2, GBA, and PRKN mutations at the Pitié-Salpêtrière Hospital between 1997 and 2009. Patients with known monogenetic forms of PD from six other centers were also included. The Mattis Dementia Rating Scale (MDRS) was used to evaluate cognition at baseline and one-year post-surgery. The standardized Unified PD Rating Scale (UPDRS) evaluation On and Off medication/DBS was also administered. A generalized linear model adjusted for sex, ethnicity, age at onset, and disease duration was used to evaluate the effect of genetic factors on MDRS changes. RESULTS: We analyzed 208 patients (131 males, 77 females, 54.3 ± 8.8 years) including 25 GBA, 18 LRRK2, 22 PRKN, and 143 PD patients without mutations. PRKN patients were younger and had a longer disease duration at baseline. A GBA mutation was the only significant genetic factor associated with MDRS change (ß = -2.51, p = 0.009). GBA mutation carriers had a more pronounced post-operative MDRS decline (3.2 ± 5.1) than patients with LRRK2 (0.9 ± 4.8), PRKN (0.5 ± 2.7) or controls (1.4 ± 4.4). The motor response to DBS was similar between groups. CONCLUSION: GBA mutations are associated with early cognitive decline following STN-DBS. Neuropsychological assessment and discussions on the benefit/risk ratio of DBS are particularly important for this population.

Exome Sequencing Reveals Signal Transduction Genes Involved in Impulse Control Disorders in Parkinson's Disease.

Introduction: Impulse control disorders (ICDs) frequently complicate dopamine agonist (DA) therapy in Parkinson's disease (PD). There is growing evidence of a high heritability for ICDs in the general population and in PD. Variants on genes belonging to the reward pathway have been shown to account for part of this heritability. We aimed to identify new pathways associated with ICDs in PD. Methods: Thirty-six Parkinsonian patients on DA therapy with (n = 18) and without ICDs (n = 18) matched on age at PD's onset, and gender was selected to represent the most extreme phenotypes of their category. Exome sequencing was performed, and variants with a strong functional impact in brain-expressed genes were selected. Allele frequencies and their distribution in genes and pathways were analyzed with single variant and SKAT-O tests. The 10 most associated variants, genes, and pathways were retained for replication in the Parkinson's progression markers initiative (PPMI) cohort. Results: None of markers tested passed the significance threshold adjusted for multiple comparisons. However, the "Adenylate cyclase activating" pathway, one of the top associated pathways in the discovery data set (p = 1.6 × 10-3) was replicated in the PPMI cohort and was significantly associated with ICDs in a post hoc pooled analysis (combined p-value 3.3 × 10-5). Two of the 10 most associated variants belonged to genes implicated in cAMP and ERK signaling (rs34193571 in RasGRF2, p = 5 × 10-4; rs1877652 in PDE2A, p = 8 × 10-4) although non-significant after Bonferroni correction. Conclusion: Our results suggest that genes implicated in the signaling pathways linked to G protein-coupled receptors participate to genetic susceptibility to ICDs in PD.

How many patients are eligible for disease-modifying treatment in Alzheimer's disease? A French national observational study over 5 years.

OBJECTIVE: We aimed to study the epidemiology of the prodromal and mild stages of Alzheimer's disease (AD) patients who are eligible for clinical trials with disease-modifying therapies. SETTINGS: We analysed two large complementary databases to study the incidence and characteristics of this population on a nationwide scope in France from 2014 to 2018. The National Alzheimer Database contains data from 357 memory centres and 90 private neurologists. Data from 2014 to 2018 have been analysed. PARTICIPANTS: Patients, 50-85 years old, diagnosed with AD who had an Mini-Mental State Exam (MMSE) score of ≥20 were included. We excluded patients with mixed and non-AD neurocognitive disorders. PRIMARY OUTCOME MEASURE: Descriptive statistics of the population of interest was the primary measure. RESULTS: In the National Alzheimer Database, 550 198 patients were assessed. Among them, 72 174 (13.1%) were diagnosed with AD and had an MMSE ≥20. Using corrections for specificity of clinical diagnosis of AD, we estimated that about 50 000 (9.1%) had a prodromal or mild AD. In the combined electronic clinical records database of 11 French expert memory centres, a diagnosis of prodromal or mild AD, certified by the use of cerebrospinal fluid AD biomarkers, could be established in 195 (1.3%) out of 14 596 patients. CONCLUSIONS: AD was not frequently diagnosed at a prodromal or mild dementia stage in France in 2014 to 2018. Diagnosis rarely relied on a pathophysiological marker even in expert memory centres. National databases will be valuable to monitor early stage AD diagnosis efficacy in memory centres when a disease-modifying treatment becomes available.

Suggestive association between OPRM1 and impulse control disorders in Parkinson's disease.

BACKGROUND: Impulse control disorders are frequently associated with dopaminergic therapy in Parkinson's disease. Genetic studies have suggested a high heritability of impulse control disorders in the general population and in PD. The aim of this study was to identify candidate gene variants associated with impulse control disorders and related behaviors in PD. METHODS: We performed a multicenter case-control study in PD patients with (cases) or without impulse control disorders and related behaviors despite significant dopamine agonist exposure of >300 mg levodopa-equivalent daily dose during 12 months (controls). Behavioral disorders were assessed using the Ardouin scale. We investigated 50 variants in 24 candidate genes by a multivariate logistic regression analysis adjusted for sex and age at PD onset. RESULTS: The analysis was performed on 172 cases and 132 controls. Cases were younger (60 ± 8 vs 63 ± 8 years; P < 0.001) and had a higher family history of pathological gambling (12% vs 5%, P = 0.03). No variant was significantly associated with impulse control disorders or related behaviors after correction for multiple testing, although the 2 top variants were close to significant (OPRM1 rs179991, OR, 0.49; 95%CI, 0.32-0.76; P = 0.0013; Bonferroni adjusted P = 0.065; DAT1 40-base pair variable number tandem repeat, OR, 1.82; 95%CI, 1.24-2.68; P = 0.0021; Bonferroni adjusted P = 0.105). CONCLUSIONS: Our results are suggestive of a novel association of the opioid receptor gene OPRM1 with impulse control disorders and related behaviors in PD and confirm a previous association with DAT1. Although replication in independent studies is needed, our results bring potential new insights to the understanding of molecular mechanisms of impulse control disorders. © 2018 International Parkinson and Movement Disorder Society.